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* Change 'sox' to 'default'

* Make tests backend aware by introducing TorchaudioTestCase and reset backend for each TestCase.

* Set backends for the test cases that require specific backend.

* Refactor backend switching

1. Do not rely on global variables for backend switch
   So that load/save/info/load_wav functions will be torchscript-able
2. Add no_backend module to for the case there is no backend module available
   [bonus] This allows the whole codebase importable on systems that do not have torchaudio C++ extension nor soundfile.

* Add cmu_arctic dataset

* add dataset name

* update audio test file with whitenoise.wav file

* add test text file

* update text method and file name

* update comment

* change datasets order in doc

* add line length
Co-authored-by: Ji Chen <jimchen90@devfair0160.h2.fair>

* Added the popular GTZAN dataset:

* Added the GTZAN class in torchaudio.datasets using the same format as the rest of the datasets.
* Added the appropriate test function in test_datasets.py.
* Added the GTZAN class in the datasets.rst documentation file.

* Addressed review issues in PR #668

* Added dummy noise .wav in `test/assets/`
* Removed transforms of input and output from the dataset
  `__init__` function, as well as the corresponding methods.
* Replaced rendundant `filtered` and `subset` methods from
  class initialization and also changed the corresponding
  assertion message.

* Fixed E303: too many blank lines error

* Added GTZAN to __init__.__all__

* Fixed incorrectly not importing GTZAN

* removed duplicate warning

* lint
Co-authored-by: Vincent QB <vincentqb@users.noreply.github.com>

* Turn on tests when building through conda-build

* skip one failling test on windows.

* Switch to unittest

* Fix conda env activation

* add linux only condition

* Fix librosa installation

* conda-forge channel as a fallback

* Revert "conda-forge channel as a fallback"

This reverts commit 8c15f6638e34792640a3de3e057d9c2995425180.

* Try the pytorch way

* Use conda-forge

* Disable conda-forge

* Install future at setup env

* Move linux unittest CI job tools to dedicated directory

* Restore Linux scripts and move the updated souces to a new subdir

* Add back future

* Remove comments
Co-authored-by: Vincent Quenneville-Belair <vincentqb@gmail.com>
Co-authored-by: moto <855818+mthrok@users.noreply.github.com>

This PR aims the following things;
1. Introduce and adopt helper function `get_asset_path` that abstract the logic to construct path to test assets.
2. Remove `create_temp_assets_dir` anywhere except `test_io`.

The benefits of doing so are,
a. the test code becomes simpler (no manual construction of asset path with `os.path.join`)
b. No unnecessary directory creation and file copies.

For 2. and b. tests in `test_io.py` (or tests that use `torchaudio.save`) are the only tests that need to write file to the disc, where the use of temporary directory makes it cleaner, therefore, `create_temp_assets_dir` is not necessary elsewhere. (still, `test_io` does not need to copy the entire asset directory, but that's not the point here.)

Also if any test is accidentally overwriting an asset data, not using a copy will make us aware of such behavior, so it is better to get rid of `create_temp_assets_dir`.

* fix download

* fix reading tsv archive

* add new languages

* maintain same structure as other datasets

* update CommonVoice Tests

* fix

* change directory name

* remove extra line

* add speechcommand dataset and test

* prepend the full path to each result

* add missing param on docstring in walk_files

* add file to run tests on SpeechCommand Dataset

* reduce logic

* update test on SpeechCommands

* correct the indentation on docstring walk_files

* flake8 compliance

* change tuple type returned. move path split logic in load item.

* typo in name.

* redundant file path.

* filter background noise.
Co-authored-by: Vincent QB <vincentqb@users.noreply.github.com>

* LJ Speech dataset

* refactoring

as per @vincentqb's suggestions

* close file.

* staying with datapoints as tuples until further notice.

* loading tsv as dict.

* BackgroundGenerator
* renaming disk cache.

Fix test for datasets.

* new dataset format.
* add basic test.
* files for testing.
* serialization using torch.
* add diskcache.
* adding deprecation warnings.
* removing legacy.
* warning about transforms.
* detecting file format using reader.